Research @ Infosys Labshttp://www.infosysblogs.com/infosys-labs/
The Infosys Labs research blog tracks trends in technology with a focus on applied research in Information and Communication Technology (ICT)enCopyright 2019Wed, 12 Dec 2018 09:17:16 +0000http://www.sixapart.com/movabletype/?v=5.14-enhttp://blogs.law.harvard.edu/tech/rssResetting Robot's Dream"Cal is a helper house-Robot owned by Mr. Northrop, an
author and technology enthusiast. Mr. Northrop is a prolific writer and
sometimes loses track of other activities, he likes the way Cal picks up after
him, runs his printer, stacks his disks, and other things. He doesn't need a
complicated robot and Cal surely fits in. But Cal is a special robot with a level
of intelligence not completely explored and with time Cal develops curiosity
and interest in writing. More like being influenced by the author persona of
his master. As Mr. Northrop comes to know Cal's interest he decides to upgrade
Cal with dictionary, vocabulary, grammar, and other essentials for writing
stuff. Cal starts writing, initially he wrote random letters like gibberish.
But with more upgrades and advice from Mr. Northrop, Cal got better and better.
After few attempts Cal wrote a satire with perfect sense of the ridiculous, Mr.
Northrop read the story 2-3 times; a sudden feeling of insecurity came to him,
what if Cal writes more stories and continues to improve each time? Mr.
Northrop decided to undo all improvements and reset Cal as it was when he
bought. "

Above is the summary of science fiction short story written by
Isaac Asimov
in 1991. He wrote many stories on robotics and often credited with devising
Three laws of Robotics, which was adapted into Hollywood sci-fiaction film "I,
Robot" starring Will Smith.

The vision on future of robotic automation and questions raised
by Asimov on freedom of choice is even more relevant in era growing practice of
AI. The core issue, that may have prompted Mr. Northrop to take the reset
route, is his inability to appreciate the robots did and the grey area around robots
decision making which is incomprehensible. Recently Facebook was experimenting
with chatbots which were to negotiate among each other for ownership of virtual
items, but after a few rounds the AI programs seemed to be interacting in a language
that only they understood; Facebook had to shut down the experiment.

Transparency is a major factor that we need to address for
building sustainable AI systems, in above case had Mr. Northrop knew that Cal
was only trying to mimic him for extending help rather than being a competition,
his action could have been different. Along with that interpretability and explainability
of decision taken by AI systems would nullify grey areas, thereby building
confidence among user community on trustworthiness of the systems. The factors
will be crucial as organizationssail
through the transformation journey of industry 4.0 where AI will have
significant penetration across industry verticals.

To stay ahead
with the AI curve, Organizations must build trust in their AI application. That
will also speed up adoption of AI application among the stakeholders within and
outside the organizations. For example, there is huge potential for AI in
banking sector. In areas like traditional loan approval value chain from
application to disbursement, AI can be applied at stages such as validation,
due diligence, and approval; but lack of trust & transparency in AI applications
hinders the adoption of AI led loan evaluation process. There are many such
cases across industries like customer recommended in retail, optimizing the
distribution of energy, fraudulent reimbursement in insurance etc.

Moving on to digital era we will be surrounded
smart AI systems and would interacting with real life CAL s for day-in day-out.
So, it's our imperative to build robust mechanism for explainability as well as
trusted and sustainable AI systems.

]]>http://www.infosysblogs.com/infosys-labs/2018/12/resetting_robots_dream.html
http://www.infosysblogs.com/infosys-labs/2018/12/resetting_robots_dream.htmlInnovationWed, 12 Dec 2018 09:17:16 +0000Rise of Emotional Intelligence in AIWe typically prefer to be with people who can understand us
and are emotionally intelligent. Body language and tone play a significant part
in what we think and feel. Emotional intelligence encompasses the ability of
people to recognize, understand and control their own emotions as well as
recognize, understand and influence others' emotions. EQ has become an
important consideration when we talk about AI development. As per Rana el
Kaliouby, co founder and CEO of Affectiva, an MIT spinout company that works on
emotional recognition technology, "If it's interfacing with a human, it needs
social and emotional skills." The addition of EQ to AI will help such systems
respond better to more complex human needs leading to creation of better
customer experiences and thereby improve customer satisfaction.

Businesses are increasingly benefitting from advances in
emotionally intelligent AI as they uncover new opportunities by understanding consumer
likes and dislikes along with gauging their affinity towards a brand or product.
As per a recent study by Market Research Future (MRFR), the global emotion
analytics market is expected to reach USD 25 billion by 2023, growing at a CAGR
of 17% between 2017 and 2023. Also, Gartner predicts that by 2022, 10% of our
personal devices will include emotional AI capabilities, up from less than 1%
in 2018. Using sentiment analysis to understand consumer perception towards a
product/brand in the offline world has remained a daunting task. Detecting
emotions from facial expressions using AI can be used as a substitute to better
understand consumer preferences and how they engage with particular brands.

Traditionally market research companies have relied on using
different methods such a surveys, trade interviews to better understand consumer
requirements. However, these methods assume a direct correlation between future
actions and what the consumers state verbally, which may not always be accurate.
In this scenario, behavioral methods are considered more objective and are
often deployed to observe a user's reaction while interacting with a
product/brand. Manually analyzing video feeds of users interacting with a
product/brand can be pretty labor intensive. Facial emotion recognition can be
useful in this scenario as they allow market research companies to record
facial expressions automatically and derive meaningful insights from them.

Disney has designed an AI-powered algorithm to gain a better
understanding of how audiences enjoy its movies, this algorithm can recognize
complex facial expressions and also predict how audiences will react for the
remaining part of the movie. As per reports, the tests processed a staggering
figure of 16 million data points derived from 3,179 viewers.

Earlier this year, Soul Machines partnered with Daimler
Financial Services to present "Sarah", a digital human as an interface to
Daimler's financial services and mobility ecosystem aiding them to deliver enhanced
customer experiences in the areas of car financing, leasing and insurance by
utilizing facial gestures and natural voice intonation.

Annette Zimmermann, vice president of research at Gartner
claimed in January 2018, "By 2022, your personal device will know more about
your emotional state than your own family." Facial analysis, voice pattern
analysis and deep learning when used together in conjunction can help decipher
human emotions with applications across a broad range of industries such as retail,
financial services, medical diagnosis, autonomous cars, fraud detection and recruitment
among others.

The shift from data-driven interactions relying heavily on
IQ to EQ-guided experiences will also present companies an opportunity to
connect with customers on a much more intimate level. However, emotions are immensely
personal and companies working in this space should be wary about consumer
concerns such as intrusion of personal space and manipulation. Suitable
psychological training for people is also required to interpret emotional
results from these machines and fix deviations as deemed appropriate.

]]>http://www.infosysblogs.com/infosys-labs/2018/12/rise_of_emotional_intelligence.html
http://www.infosysblogs.com/infosys-labs/2018/12/rise_of_emotional_intelligence.htmlInnovationFri, 07 Dec 2018 06:31:21 +0000Explainable AI - Introduction and applicationsAI systems have essentially remained black boxes, with deep
learning models frequently remaining opaque. It has become imperative to build
systems which can justify their decisions, very similar to how humans operate. Significant
advances in this area will result in the evolution of autonomous systems that are
able to learn, make decisions and implement them without the support of any
external agents. Explainable AI (XAI) is artificial intelligence that is
programmed to describe its purpose, rationale and decision-making process in a
way that can be understood by the average person. Powerful algorithms often churn
out useful results, without explaining how they arrived at it. Thus, transparency
is often compromised while arriving at sophisticated experimental results using
AI systems. As AI models become more complex, it is important for these systems
to provide verifiable explanations of the decisions they make. Key benefits
derived from the implementation of XAI include:

·Aid in faster and broader deployment of AI

·Bring convenience and speed to consumers along
with building trust

·Adoption of best practices around the areas of
compliance, accountability and ethics

·Reduce impact of biased algorithms

The figure below illustrates the concept of XAI as
demonstrated by Defense Advanced Research Projects Agency (DARPA):

Source: XAI Concept by DARPA

AI systems have multiple applications across industries. For
example, in the financial services domain it will be important for AI systems
to be able to explain their decision making in order to be fully embraced and
gain trust in the industry. If a loan application process is denied by an
automated system powered by AI, bank executives should be able to trace the
decision to the specific step where the denial occurred and also provide a
reasoning for the AI system's decision at that particular step.

An AI system which is determining the premium charges for
car insurance should also be able to provide the rationale behind such a decision
based on several factors including age, gender, car type, accident history,
address, mileage etc. It should also aid in providing a personalized experience
by mentioning what the customer needs to do in order to reduce premium charges,
for example drive accident free for the next one year.

An ethical risk is also prevalent in this scenario as bias
can unintentionally creep into algorithmic models and thereby result in
discriminatory practices. This puts organizations at risk as consumers are
likely to switch brands once they understand about these prejudices. For
example, certain existing AI algorithms imposed higher charges for Asian
Americans opting for SAT tutoring. Facial recognition software is being
increasingly used for law enforcement and is also promulgating racial and
gender bias. Earlier this year, Joy Buoalamwini from the Massachusetts Institute
of Technology showed that gender-recognition AIS from IBM, Microsoft and
Chinese company Megvii were able to identify gender from a photograph for white
men with an accuracy of 99%. However, this number was staggeringly low at 35%
for dark-skinned women. This poses increased risk towards false identification
of women and minorities.

Explainable AI will thus help to build models which can
identify relevant stakeholders and the information they require about how the
model arrives at decisions. This would also identify any form of bias which has
crept in and aid data scientists weed them out at an early stage. Eventually as
humans and machines work together more effectively, it will be imperative for us
to understand the machine logic lying underneath.

Transparency will become an important requirement to keep up
with compliance regulations. For example, the General Data Protection
Regulation (GDPR) with a focus on right to explanation mandates that users
should be able to demand data behind algorithmic decisions made by recommendation
engines. This puts the onus on companies to translate complicated reasoning
behind AI algorithms to simple and easily interpretable language.

Our computers have become windows through which we can gaze upon a world that is virtually without horizons or boundaries.

~ Joseph B. Wirthlin

Ever complained about standing in queues and having to sign countless banking forms? Ever wondered why you need to walk into a branch of your bank to perform mundane tasks which can be easily done with a tap on your smartphone? If you have, then you are not alone. Welcome to the new world of banking where several startups and even some traditional banking giants are experimenting with all online and fully functional digital banks. These digital banks are industriously working towards addressing many of the consumer pain points in the business as usual banking world.

Digital-only banks are on the rise. And by digital-only banks, I am referring to truly digital banks who do not have any brick and mortar presence. They have an edge over traditional banks in terms of efficiency, speed, ROI and scale. Being fully automated, they are more efficient than their traditional counterparts where still a lot of processing is manual. Digital banks deliver services faster than a brick and mortar business while being efficient at the same time.

Setting up a digital bank is again more pocket friendly when compared to a regular bank. Also, a digital bank can be scaled up to meet rising consumer demands much more easily. Another major advantage is in terms of readily available data. Digital banks can undertake AI/ML initiatives with much more ease to deliver faster insights and implement changes with reduced time to market. Data cleansing and curation process becomes seamless.

Digital banks are very much powered by technology and are at the forefront of experimenting with all the cutting-edge advances available. AI powered virtual assistants, digital only cash, peer-to-peer lending and payments and blockchain based banking transactions have all been tried out in digital banking arena. Touch ID serves as an efficient security tool which provides a safe way to login to banking accounts.

Digital banks come in all flavors. Some banks offer zero maintenance fees. Few others offer rewards based on your social media likes. Revoult, a digital-only bank, offers global payments and cryptocurrency exchange. Yet another section of banks cater to a very niche segment of consumers. For example, USAA is a member only bank serving US military members and their kin.

Banking is at the cusp of a digital revolution similar to what the retail industry witnessed few years ago. Amazon disrupted brick and mortal retail stores with an all-online market. Likewise, digital banks are all set to disrupt traditional banks. Agreed there are rigid regulations and compliance in place which digital banks will have to adhere to; yet they are flourishing at a super fast pace. Banking as a platform or banking as a service may become the norm in banking sooner than later.

]]>http://www.infosysblogs.com/infosys-labs/2018/09/rise_of_digital_banks.html
http://www.infosysblogs.com/infosys-labs/2018/09/rise_of_digital_banks.htmlInnovationSun, 30 Sep 2018 23:54:00 +0000Sports and TechnologyTechnology has made its way into aspect of our life. It has
changed the way we work, travel and live. During this apparent transformation
sports, an aspect of our lives enjoyed by all has been leveraging technology to
enhance their performance and reach out to their loyal fans.

The introduction of television into our lives was a game
changer for sports. TV's enables viewers to watch the game from the comfort of
their homes and follow their favorite teams no matter where they played. It
also enabled fans to access and learn the various sports played around the
world. Whether it was sports like soccer or F1, television increased their fan
base like no other technology had ever before. New revenue streams were created
for the sports teams and associations through the sale of broadcasting rights.

Many sports have also embraced technologies for providing replays,
to review umpire decisions and such predict the direction of the ball. The
Hawk-Eye system used to predict the direction of the ball has already been
embraced by various sports such as tennis and cricket.

But, a new wave of technology is promising to revolutionize
the way we enjoy our sports. Startups are designing jerseys which the fan can
wear to feel the intensity of the game through haptic feedback which is
generated by the adrenaline and excitement of their favorite NFL team. The
technology brings the feel of a stadium to fans watching the game from home.
Another such application used the NFL is Be the Player, the application allows fans
to watch the game from the point of view of their favorite player without
having the player wear a camera.

Sports Associations are revolutionizing fan engagement by
leveraging social media sites and virtual games. Fans can now get personalized
feed of the NFL games enabling fans to select players they want to follow and
watch off the field clips of the team in the locker room and post win parties.
The NBA is engaging fans on the internet by counting votes through social
media, google, etc. to select players for the All-Stars game. They even have a
chatbot which can show clips of players or matches based on requests from fans.

Virtual reality is another aspect being embraced by technology.
Games are now being telecast for fans to watch through VR in order to provide a
more immersive experience for remotely viewing fans.

The emergence of new technology and the demand for higher
levels of engagement from fans are forcing sports teams and associations to
search and identify new channels of engagement. With the rapid rate of adoption
of technology, it won't be long before we will be able to immerse ourselves in
the excitement of the game and be closer to our favorite players than ever before.

https://www.business2community.com/sports/how-the-nba-is-on-the-ball-with-customer-engagement-0131836/amp ]]>http://www.infosysblogs.com/infosys-labs/2018/09/sports_and_technology.html
http://www.infosysblogs.com/infosys-labs/2018/09/sports_and_technology.htmlWed, 26 Sep 2018 13:42:16 +0000Artificial Intelligence & Financial Services - The ApplicationsIn my previous blog "Artificial Intelligence & Financial
Services - The Business Case" we explored the business case for adoption of AI
in the financial industry. In this blog we will look at the technology, its
applications and its adoption by the industry.

The most mature use cases are in chatbots in the front
office, antifraud and risk and KYC/AML in the middle office, and credit
underwriting in the back office.

Front office operations have leveraged chatbots to
revolutionize customer relationship management. Other than assisting customers
with their transactions, chatbots enable banks to segment customers
individually rather than general buckets by collecting data regarding their
behavior and habits. Infact, Nina, Swedbank's AI chatbot was deployed to assist
customers 2 years ago. Already, Nina has successfully demonstrated its ability
to resolve 78% contact resolution and has a customer adoption rate of 30,000
conversations per month. By 2024, 42.82% of the estimated $1.25 billion market
for chatbots is expected to be generated by the rising need for enhancing the
customer services to retain existing customers and attracting potential
customers.

Similarly, AI based anti-fraud and KYC/AML applications have
been gaining traction in middle office operations thanks to the superior
cognitive capabilities of AI. The digitization of banking products and services
has led to an increased susceptibility to fraud. Using AI significantly reduces
the time taken to review transaction, including all the factors and relevant
data associated with it. Lloyds Banking Group is using AI models that detect
when the person logged in is not the customer, but a fraudster or a bot.
Similarly, Natwest has been using AI to reduce fraudulent transactions and
reported that AI has prevented 7 million Pounds of false payments.

Back office operations too are undergoing a change. AI is
used for applications such as credit and risk underwriting in the back office
by creating a more complete and unbiased assessment of an applicant's credit
worthiness. AI based underwriting provides a more comprehensive view of the assessee'
s credit worthiness. Startup, Lenddo has already enabled their partners to
assess 5 million applicants through the 12,000 variables collected from
alternative sources.

AI has also been widely adopted by hedge funds for
algorithmic trading, AI collects data from several sources to create a more
accurate prediction. They are also being used by banks to discover investment
opportunities by scouring the markets. CircleUp, a venture capital firm has
created Classifier, a machine learning crowdfunding platform to determine which
companies to fund. The Classifier has the capability to review 500
opportunities per month with a team of less than 10 analysts vs the 500
evaluations per year done by the average private equity firm.

While chatbots, anti-fraud, risk management, KYC/AML, credit
underwriting and asset management are being adopted by the financial industry,
the other applications are generating a lot of interest and it won't be long
before they too go mainstream. With AI estimated to add more than 1 trillion in
value to the financial industry it won't be long before robots cater to our
financial needs while algorithms manage our daily finances.

The new wave of innovation and technology commonly referred
to as "fintech" is reshaping the financial services industry and forcing the
critical financial intermediaries to adopt emerging technologies. AI has been
the focus of financial institutions due to the opportunities arising from the rise
in volume of data, speed of access to it and the emergence of new and advanced
algorithms able to analyse data in a more intelligent. Industries are
leveraging the technology to gain a competitive advantage against their peers
by improving speed, cost efficiency and accuracy of processes and meeting rising
customer expectations.

The highest adoption rates of AI in financial services
companies are in IT with 63.5%, finance and accounting with 40.4%, marketing
with 31.4% and customer services with 30.8%. Challenges with the adoption of AI
in the financial service industry has been the auditability and traceability of
the applications. Financial institutions have to comply with regulations requiring
them to explain their decisions to customers and report the same to regulators.

Cost savings is expected to be the primary driver of AI in
the financial industry, analysts estimate that AI will save the banking
Industry $1 trillion in savings by 2035 with most of the savings coming from
the front office. Although the changes are predicted to be gradual until 2025,
the adoption rate is expected to accelerate until 2030.

Reduction in the scale of retail branch networks and other
distribution staff will generate most of the savings in the front office with
$199 billion. Chatbots are expected to take over and handle upto 85% of the
world's customer interaction. Chatbots are already being leveraged to help
customers manage their personal finances, provide investment advice and suggest
the best product for the customer while enabling the customer to perform simple
transactions effortlessly.

The application of AI for compliance, KYC/AML and data
processing is forecasted to save $217 billion in the middle office. One of the
mature applications, KYC/AML uses pattern detection d unstructured text
analysis to identify potential fraudulent activity in real time while identifying
complex linkages between entities.

Back office operations are also expected to generate $200
billion in savings of which $31 billion will be generated through the application
of AI for underwriting and collection systems. AI enables underwriters to
collect data from alternative sources such as social media and geolocation data
enabling to assess candidates with limited credit history and speed up the
entire process.

Applications of AI range from customer service through AI
assistants to process automation tools for eliminating time intensive work. Irrespective
of whether its intelligent automation for repetitive manual tasks, the enhanced
judgement and improved interactions provided by AI is the future of the
industry and will drive enterprise growth and profitability in the years to
come.

In my next blog "Artificial Intelligence & Financial
Services - The Applications", we will dive deeper into the application of AI in
financial services.

]]>http://www.infosysblogs.com/infosys-labs/2018/09/artificial_intelligence_financ.html
http://www.infosysblogs.com/infosys-labs/2018/09/artificial_intelligence_financ.htmlWed, 26 Sep 2018 13:28:28 +0000Journey towards Adaptive CareHistorically healthcare has been intermittent and reactive in nature. Even in today's world of digital, mobile, and technological breakthroughs (both medical sciences and ICT) when it comes to personal care people tends to follow a reactive to disease approach. That might be suitable for a sick care scenario but journey towards continuous and proactive healthcare will require a more connected environment, personalization, better patient experience, home care, and predictive medications. Rather than relying on intermittent data for diagnostics decision, patient should be at the center of care system which will give a complete view of the patient's biological, physical, mental status.

]]>http://www.infosysblogs.com/infosys-labs/2018/09/journey_towards_adaptive_care.html
http://www.infosysblogs.com/infosys-labs/2018/09/journey_towards_adaptive_care.htmlInnovationWed, 26 Sep 2018 04:56:44 +0000V2X Next wave of transportationAdvancement in the societal and new market trends leading to revolution in personal mobility and vehicular transport system. Societal trends such as rapid growth of urbanization putting pressure on current transportation setup, which is growing less compared to the demand, tough emission and energy related regulation are also impacting transportation systems. Apart from this, market trends as advancement of automated driving, real-time and open data accessibility, enabling more effective use of transport assets and also affecting the current transportation systems.]]>http://www.infosysblogs.com/infosys-labs/2018/09/v2x_next_wave_of_transportatio.html
http://www.infosysblogs.com/infosys-labs/2018/09/v2x_next_wave_of_transportatio.htmlInnovationFri, 21 Sep 2018 09:29:04 +0000Platformization, the new frontier for IT servicesIt was another busy day at office for Mr. Nayak, at 6 PM it's time to start for home. But, he is not going home today, how can he forgot his anniversary; he specifically set a reminder for it, after the goof up of last year. Mr. Nayak searches for wine & dine restaurants in his smartphone, the app automatically suggests him options for buying flowers and chocolates around the searched location.]]>http://www.infosysblogs.com/infosys-labs/2018/09/platformization_the_new_fronti.html
http://www.infosysblogs.com/infosys-labs/2018/09/platformization_the_new_fronti.htmlNext Generation Application ManagementFri, 21 Sep 2018 09:06:01 +0000Adaptive Inspection: Hurricane Season 2017, hurricane Harvey and Irma hit the US coast with
winds exceeding 130 miles per hour, leaving in its wake 103 people dead and an
estimated damage of $200 billion. The double
whammy within 2 months of each other and the severity of the hurricanes is
expected to slow US GDP by 1%.

In Florida alone, the total insured losses were estimated at
more than $5.8 billion, with more than 689,000 residential property claims and
51,396 commercial property claims due to Hurricane Irma. Insurance companies
were inundated with claims and scrambled to process the claims submitted by
their customers. The frenzy was aggravated by the fact that Hurricane Harvey
had hit Texas less than 3 weeks before Hurricane Irma hit Florida.

One of the greatest challenges that Insurers faced during
the 2017 hurricane season was the shortage of adjusters. The first step for
insurers to process claims was to have the adjusters visually assess the damage
and estimate the loss. Unfortunately, most of the adjuster were in Texas
assessing damage due to Hurricane Harvey leading to a shortage of adjusters in
Florida and an increase in adjuster prices in the range of 15% to 25%. The
shortage was only amplified by the lack of access and safety concerns.

Adaptive Inspection technologies which combine the
capabilities of artificial intelligence in the form of computer vision and image
analytics, and edge computing enable insurance companies to leverage autonomous
agents such as drones to inspect property claims more efficiently and
effectively. Drones are capable of flying closer to structures to capture
miniscule details through high resolution images providing a more thorough
report than humans adjusters while reducing the time from 1 hour to 15 minutes.
Edge computing capabilities enable the drones to avoid obstacles, reach the
location and provide images for the image analytics to analyse, estimate damage
and create coverage reports.

his process lays redundant the erstwhile paper based
process resulting in errors, and speeds up the claims process while preventing
adjuster injuries. The technology can also be used to assess property damage in
calamity affected areas before receiving claim requests in order to speed the
process and prevent consumer grief.

Companies like USAA, AIG and Allstate have already deployed
drones to enable adjusters to view hard to reach areas from a safe location and
analyse the images. The technology has rapidly matured over the years and
stands to change the way adjusters and insurance companies assess claims while
changing the way organizations all over the world inspect their physical
assets.

The
Facebook data breach saga became a global phenomenon with reports suggesting
that more than 87 million user profiles were compromised. The company lost over
14% of its market capitalization with #DeleteFacebook trending on Twitter. With
over 3 billion active users on various social media platforms, this data breach
might just be the tip of the iceberg.

]]>http://www.infosysblogs.com/infosys-labs/2018/06/blockchain_next_social_media_a.html
http://www.infosysblogs.com/infosys-labs/2018/06/blockchain_next_social_media_a.htmlSecurity & PrivacyMon, 04 Jun 2018 11:11:56 +0000Facial biometrics going mainstream...Recognizing someone by sight has been the building block of
human interaction and more importantly has helped conduct commerce through the
course of known history. It has helped build trust over time and eased many
interactions and transactions. Of course, humans carry their very own powerful
computer that instantly helps them recognize, recollect, build context and
communicate effectively. In the recent times however, interactions with
machines have increased substantially bringing in the need for many artificial
means to establish identity - mechanisms such as cards, passwords, finger
prints etc. While these have helped to an extent, humans have had to learn new
ways to interact with systems while also opening up potential loop holes for
exploitation. ]]>http://www.infosysblogs.com/infosys-labs/2018/05/facial_biometrics_going_mainst.html
http://www.infosysblogs.com/infosys-labs/2018/05/facial_biometrics_going_mainst.htmlThu, 03 May 2018 07:17:00 +0000Quantum Computing- The next computing revolutionIn a conference hosted by MIT's Laboratory for Computer
Science in 1981, Richard Feynman proposed the concept of computers which would
harness the strange characteristics of matter at the atomic level to perform
calculations. Last year, IBM open-sourced its quantum computing network called
the IBM Q- Experience to encourage researchers and enterprises to explore
various possibilities of quantum computing. Other companies like Google,
Microsoft and Intel are also in the race to build their own quantum computer to
leverage its exceptional computing capabilities.]]>http://www.infosysblogs.com/infosys-labs/2018/05/quantum_computing-_the_next_co.html
http://www.infosysblogs.com/infosys-labs/2018/05/quantum_computing-_the_next_co.htmlThu, 03 May 2018 06:50:11 +0000Cognitive System-Mimicking Human UnderstandingWith advancements in artificial intelligence algorithms, it's
possible for machines to mimic human understanding. They are able to analyze
and interpret information, make deductions and identify patterns from the
information sets analogous to human brain. These new generation of machines are
categorized as cognitive systems. These systems aggregate machine intelligence,
predictive analytics, machines learning, natural language engines and image/video/text
analytics to enhance human-machine interaction.]]>http://www.infosysblogs.com/infosys-labs/2018/03/cognitive_system-mimicking_hum.html
http://www.infosysblogs.com/infosys-labs/2018/03/cognitive_system-mimicking_hum.htmlThu, 29 Mar 2018 10:04:12 +0000